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IOweb3 Technologies Pvt Ltd

IOweb3 Technologies Pvt Ltd

Agency
India flagPune, India
$10.00/hrExpert110+

Key Skills

Software

AWS SageMakerAWS SageMaker
Anno-MageAnno-Mage
AppenAppen
ArgillaArgilla
Axiom AI
ClickworkerClickworker
CloudFactoryCloudFactory
CrowdFlowerCrowdFlower
CrowdSourceCrowdSource
CVATCVAT
Data Annotation TechData Annotation Tech
DataloopDataloop
DatatroniqDatatroniq
DatumboxDatumbox
DatasaurDatasaur
DatatureDatature
DataturkDataturk
Deep SystemsDeep Systems
DiffgramDiffgram
DoccanoDoccano
EncordEncord
Figure EightFigure Eight
Google Cloud Vertex AIGoogle Cloud Vertex AI
HastyHasty
HiveMindHiveMind
HumanaticHumanatic
iMeritiMerit
Img Lab
Kili TechnologyKili Technology
LabelboxLabelbox
LabelImgLabelImg
Label StudioLabel Studio
LightTagLightTag
LionbridgeLionbridge
Mighty AIMighty AI
MindriftMindrift
OneFormaOneForma
OpenCV AI Kit (OAK)OpenCV AI Kit (OAK)
PlaymentPlayment
ProdigyProdigy
Redbrick AIRedbrick AI
RemotasksRemotasks
RoboflowRoboflow
SamaSama
Scale AIScale AI
SlothSloth
Snorkel AISnorkel AI
SuperAnnotateSuperAnnotate
SuperviselySupervisely
Surge AISurge AI
TagtogTagtog
TolokaToloka
TelusTelus
Trilldata Technologies
VoTT
V7 LabsV7 Labs
Other
Internal/Proprietary Tooling

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code ProgrammingComputer Code Programming
TextText
VideoVideo

Top Task Types

Bounding Box
Classification
Computer Programming Coding
Data Collection
Evaluation Rating

Company Overview

We provide the high-quality, human-powered data you need to build better AI models. We manage the process so you can focus on innovation.

ExpertThaiUrduZuluSwahiliCatalanDutchGreekHindiIrishMalayTamilUzbekWelshArabicAwadhiDanishFrenchGermanHebrewKazakhKoreanMagahiNepaliPashtoPolishTeluguUyghurZhuangBalochiBengaliBurmeseEnglishFinnishItalianKannadaKurdishMarathiPunjabiRussianSlovenianSpanishSwedishTurkishYiddishAlbanianArmenianAssameseBhojpuriEstonianJapaneseRomanianCantoneseMalayalamNorwegianSinhaleseSundaneseIndonesianPortugueseVietnameseAzerbaijaniOdia OriyaChhattisgarhiGreek ModernPersian FarsiSudanese ArabicChinese Mandarin

Security

Security Overview

At io-ai, security is a foundational principle. Our framework is built on a "Security by Design" and "Zero Trust" model to protect client data and intellectual property across our global, distributed workforce. Cybersecurity & Infrastructure Our platform leverages secure cloud infrastructure (AWS/GCP) with strict firewalls and network access controls. All client data is encrypted both in-transit (TLS 1.2+) and at-rest (AES-256). Access to data and systems is governed by Multi-Factor Authentication (MFA) and the principle of least privilege (RBAC), ensuring users can only access information essential to their role. Data Handling & Confidentiality Every expert in our global network is bound by a comprehensive Non-Disclosure Agreement (NDA) before accessing any client project. All experts must complete mandatory training on data privacy, secure handling protocols, and client confidentiality. Client data is logically segregated by project, minimized to what is necessary, and is never permitted to be stored permanently on local workstations. Compliance & Audits Our security program is aligned with the principles of leading international standards, including GDPR, SOC 2, and ISO 27001. We validate our controls through regular internal security audits and engage independent third-party firms for vulnerability assessments and penetration testing. Secure Remote Operations As a remote-first organization, our security model prioritizes robust endpoint protection (managed devices, antivirus, disk encryption) and clear protocols for our experts on maintaining a private and secure physical workspace. This modern approach ensures a resilient and secure operational environment for all client projects.

Labeling Experience

Global Speech Data Collection & Annotation for Multi-Language Conversational AI

Internal Proprietary ToolingAudioAudio RecordingTranslation Localization
Currently executing a large-scale, multi-language data collection and annotation project for a leading cloud provider (AWS) to expand their Conversational AI capabilities across 30+ global languages. The project scope involves delivering a target of 40,000 high-quality speech utterances per language. Our team is managing the end-to-end global workflow: Global Recruitment: Actively sourcing, vetting, and managing a diverse network of native speakers across dozens of countries to ensure authentic dialectal and demographic representation. Multi-Language Data Collection: Coordinating the ongoing creation and recording of utterances focused on 10 specific Inverse Text Normalization (ITN) categories (e.g., currencies, dates, addresses). Localized Annotation: Deploying language-specific teams to perform dual-layer annotation for each audio file: a precise verbatim transcription and a fully normalized ITN version. Centralized Quality Assurance: We have implemented a scalable QA framework

Currently executing a large-scale, multi-language data collection and annotation project for a leading cloud provider (AWS) to expand their Conversational AI capabilities across 30+ global languages. The project scope involves delivering a target of 40,000 high-quality speech utterances per language. Our team is managing the end-to-end global workflow: Global Recruitment: Actively sourcing, vetting, and managing a diverse network of native speakers across dozens of countries to ensure authentic dialectal and demographic representation. Multi-Language Data Collection: Coordinating the ongoing creation and recording of utterances focused on 10 specific Inverse Text Normalization (ITN) categories (e.g., currencies, dates, addresses). Localized Annotation: Deploying language-specific teams to perform dual-layer annotation for each audio file: a precise verbatim transcription and a fully normalized ITN version. Centralized Quality Assurance: We have implemented a scalable QA framework

2025

Agentic Workflow Creation for Advanced LLM Reasoning and Tool Use

Internal Proprietary ToolingComputer Code ProgrammingComputer Programming CodingFunction Calling
Developed agentic AI workflows to train advanced LLM reasoning. Our core responsibilities included: - Engineering system prompts and defining available tools (APIs). - Creating the AI's internal reasoning via Chain-of-Thought (CoT). - Generating sequential, structured API function calls. - Enforcing strict data grounding to ensure hallucination-free outputs.

Developed agentic AI workflows to train advanced LLM reasoning. Our core responsibilities included: - Engineering system prompts and defining available tools (APIs). - Creating the AI's internal reasoning via Chain-of-Thought (CoT). - Generating sequential, structured API function calls. - Enforcing strict data grounding to ensure hallucination-free outputs.

2025 - 2025

Scene Cut Annotation Project

OtherVideoSegmentation
This project involved annotating scene boundaries in a large batch of YouTube videos to support video content analysis and editing workflows. Video editors used the uLabel tool by Uber AI Solutions to mark the exact timestamps of scene cuts and transitions, segmenting continuous video streams into distinct scenes. The goal was to produce high-quality temporal segmentation labels to enable downstream tasks like content summarization, video indexing, and automated editing. A team of 14 annotators followed strict guidelines for consistency, with periodic quality checks and inter-annotator agreement reviews to ensure accuracy and uniformity across the dataset.

This project involved annotating scene boundaries in a large batch of YouTube videos to support video content analysis and editing workflows. Video editors used the uLabel tool by Uber AI Solutions to mark the exact timestamps of scene cuts and transitions, segmenting continuous video streams into distinct scenes. The goal was to produce high-quality temporal segmentation labels to enable downstream tasks like content summarization, video indexing, and automated editing. A team of 14 annotators followed strict guidelines for consistency, with periodic quality checks and inter-annotator agreement reviews to ensure accuracy and uniformity across the dataset.

2025 - 2025

Rubric Review Project

OtherTextEvaluation Rating
This project involved reviewing and evaluating rubric properties for AI-generated responses to user prompts. Annotators were given prompts, AI-generated rubric properties, and had to identify incorrect, invalid, irrelevant, or missing properties, rate their severity, merge or remove duplicates, and rewrite rubrics where needed. The project ensured rubric accuracy, completeness, and alignment with prompt intent and standards through quality checks and adherence to detailed review guidelines.

This project involved reviewing and evaluating rubric properties for AI-generated responses to user prompts. Annotators were given prompts, AI-generated rubric properties, and had to identify incorrect, invalid, irrelevant, or missing properties, rate their severity, merge or remove duplicates, and rewrite rubrics where needed. The project ensured rubric accuracy, completeness, and alignment with prompt intent and standards through quality checks and adherence to detailed review guidelines.

2025 - 2025